Prediction-Error Negativity to Assess Singularity Avoidance Strategies in Physical Human-Robot Collaboration
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021, 2021-May, pp. 3241-3247
- Issue Date:
- 2021-10-18
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Prediction-Error_Negativity_to_Assess_Singularity_Avoidance_Strategies_in_Physical_Human-Robot_Collaboration.pdf | Published version | 6.01 MB |
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In physical human-robot collaboration (pHRC), singularity avoidance strategies are often critical to obtain stable interaction dynamics. It is hypothesised a predictable singularity avoidance strategy is preferred in pHRC as humans tend to maximise predictability when using complex systems. By using an electroencephalogram (EEG), it is possible to assess the predictability of a task through a feature found in event-related potentials (ERP) and called prediction-error negativity (PEN). In this paper, two research questions are addressed. Can a complex pHRC singularity avoidance strategy generate a detectable PEN? Are PEN and human preferences related when comparing different control settings in a singularity avoidance strategy? Fourteen participants compared two different sets of parameters (modes) in a singularity avoidance strategy based on the exponentially damped least-squared (EDLS) method. ERP results are presented in terms of power spectral density (PSD). ERP results were then compared with human preferences to see whether they are related. Results show that the mode that causes PEN is also the one that participants did not like, suggesting that a lack of predictability might have an impact on human preference.
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